AFFECTING MILLIONS OF PEOPLE AROUND THE WORLD, THE CARDIOVASCULAR DISEASES ARE RESPONSIBLE FOR THE MAIN CAUSE OF HOSPITALIZATION AND DEATH IN MANY COUNTRIES, BEING A SERIOUS PUBLIC HEALTH PROBLEM. MANY PATIENTS AFFECTED WITH SOME CARDIOVASCULAR DISEASES AND NOT ELIGIBLE FOR HEARTH TRANSPLANTATION, RECEIVE A KIND OF CIRCULATORY DEVICE FOR LIFE SUPPORT KNOWN AS VENTRICULAR ASSIST DEVICE (VAD). THIS KIND OF DEVICE HAS SOME PROBLEMS, AMONG THEM THE THROMBOGENESIS, WHICH CONSISTS IN THE OCCURRENCE OF THROMBUS INSIDE OF DEVICE. THIS SERIOUS PROBLEM CAN LEAD TO DISABLEMENT OF DEVICE OR EVEN CAUSE PATIENT€™S DEATH. HOWEVER, THE PREVIOUS AND NOT INVASIVE DIAGNOSTIC IS SOMETHING RELATIVE COMPLEX. THIS WORK AIMS TO DEVELOP A PYTHON ALGORITHM TO REPRODUCE SIGNALS THAT INDICATE PRESENCE AND ABSENCE OF THROMBUS SHOWED ON. THESE SIGNALS WERE USED TO TRAIN A NEURAL NETWORK TO CLASSIFY ABSENCE OR/AND PRESENCE OF THROMBUS.